Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: establishing, by a blockchain system, a smart contract on the blockchain system for a network resource in a computer environment for an action to be performed on the network resource if a malware is detected in the network resource, wherein the computer environment is a network system that is a separate system from the blockchain system; in response to the malware being detected in the network resource, determining, by the blockchain system, whether a consensus is reached by a plurality of computers on the blockchain system to implement the action to contain the malware based on the smart contract; and in response to the consensus being reached by the plurality of computers, transmitting, from the blockchain system to the network resource, directions to implement the action on the network resource as specified by the smart contract.
2. The method of claim 1 , further comprising: generating, by one or more processors, a network resource graph of the network resource, wherein the network resource graph depicts connections between nodes that depict network devices in the network resource; generating, by one or more processors, a dominant nodes graph of dominant nodes in the network resource graph, wherein the dominant nodes graph depicts a dominance frontier of the nodes in the network resource graph; applying, by one or more processors, a set of network resource policies on the dominant nodes graph in order to determine a set of paths from a source node to a destination node; applying, by one or more processors, a dominance frontier analysis from the source node to calculate sets of nodes in the network resource that can be affected by the malware; utilizing, by one or more processors, a machine learning system to determine potential malware spread pathways of the malware through resources depicted by the sets of nodes in the network resource that can be affected by the malware; determining, by one or more processors, a particular set of nodes, from the sets of nodes in the network resource that can be affected by the malware, that represent resources that need to be shut down based on a cost based model using a K-min cut algorithm; and shutting down, by one or more processors, the resources that are represented by the determined particular set of nodes.
3. The method of claim 1 , wherein the network resource is a datacenter.
A method for managing network resources, specifically datacenters, involves dynamically allocating and deallocating resources based on demand to optimize performance and cost efficiency. The method includes monitoring the usage of the datacenter resources, such as computing power, storage, and network bandwidth, to detect fluctuations in demand. When demand increases, additional resources are automatically allocated to maintain performance, while excess resources are deallocated during low-demand periods to reduce costs. The system uses predictive analytics to anticipate future demand patterns, allowing for proactive resource adjustments. This approach ensures that the datacenter operates efficiently, avoiding over-provisioning or under-provisioning of resources. The method also integrates with existing infrastructure management tools to streamline operations and provide real-time visibility into resource utilization. By dynamically adjusting resources, the system minimizes downtime, improves scalability, and reduces operational expenses. The solution is particularly useful for cloud-based datacenters where workloads vary significantly over time.
4. The method of claim 1 , wherein the network resource is a component of a datacenter, and wherein the network resource is from the group consisting of a computer, a network port, a switch and a virtual machine used by the datacenter.
A method for managing network resources in a datacenter environment addresses the challenge of efficiently monitoring and controlling various components to ensure optimal performance and security. The method involves identifying and tracking network resources, which include computers, network ports, switches, and virtual machines used within the datacenter. These resources are dynamically monitored to detect changes in their status, such as availability, performance metrics, or security vulnerabilities. The method further includes applying predefined rules or policies to these resources to enforce compliance, allocate bandwidth, or mitigate risks. For example, if a virtual machine exhibits unusual traffic patterns, the system may isolate it or adjust its network access. Similarly, a switch or network port may be reconfigured to prioritize critical traffic or block suspicious activity. The approach ensures that datacenter operations remain resilient, secure, and efficient by continuously adapting to the state of its network resources. This method is particularly useful in large-scale datacenters where manual oversight is impractical, providing automated and scalable management of diverse network components.
5. The method of claim 1 , wherein the network resource is a datacenter, wherein the plurality of computers are used by a plurality of users that direct the blockchain system to establish the smart contract, and wherein the plurality of users comprise a client user of the datacenter, a cloud administrator of a cloud upon which the datacenter resides, a compliance officer for the datacenter, and a security manager for the datacenter.
6. The method of claim 1 , wherein the smart contract codifies policies and requirements for network resource availability, network resource shutdown, and network resource incidence responses for the network resource.
7. The method of claim 1 , wherein ledgers on the blockchain system keep track of malware propagation information about the malware, probabilistic indicators of compromise about the malware, and identification information about the malware.
This invention relates to blockchain-based systems for tracking and analyzing malware threats. The system addresses the challenge of detecting and mitigating malware propagation by leveraging distributed ledger technology to record and share threat intelligence across multiple entities. The blockchain system maintains ledgers that store detailed information about malware, including propagation patterns, probabilistic indicators of compromise (IOCs), and unique identification data. By decentralizing this information, the system enables real-time sharing of threat intelligence among participants, improving detection accuracy and response times. The ledgers are structured to capture how malware spreads, the likelihood of compromise based on observed behaviors, and identifiers such as hashes or signatures. This approach enhances cybersecurity by providing a tamper-resistant, transparent record of malware activity, allowing organizations to correlate threats and take coordinated action. The system may also integrate with existing security tools to automate threat detection and response based on the blockchain-stored data. The invention aims to reduce the impact of malware by fostering collaboration and improving the reliability of threat intelligence.
8. The method of claim 1 , wherein the computer environment is a cloud environment.
A system and method for managing computational resources in a cloud environment addresses the challenge of efficiently allocating and optimizing resource usage in distributed computing systems. The invention provides a dynamic resource allocation mechanism that monitors workload demands and automatically adjusts computing resources, such as virtual machines or containers, to improve performance and reduce costs. The system includes a monitoring module that tracks resource utilization metrics, such as CPU, memory, and network usage, and an optimization engine that analyzes these metrics to determine the optimal allocation of resources. The optimization engine may scale resources up or down based on demand, migrate workloads to different nodes for load balancing, or terminate underutilized resources to minimize costs. Additionally, the system may incorporate predictive analytics to anticipate future demand and preemptively adjust resources. The cloud environment enables seamless scalability and flexibility, allowing the system to handle varying workloads efficiently. By automating resource management, the invention reduces manual intervention, enhances system efficiency, and ensures cost-effective operation in a cloud-based infrastructure.
9. A computer program product for containing malware in a network resource, wherein the computer program product comprises a non-transitory computer readable storage device having program instructions embodied therewith, the program instructions readable and executable by a computer to perform a method comprising: establishing, by a blockchain system, a smart contract on the blockchain system for a network resource in a computer environment for an action to be performed on the network resource if a malware is detected in the network resource, wherein the computer environment is a network system that is a separate system from the blockchain system; in response to the malware being detected in the network resource, determining, by the blockchain system, whether a consensus is reached by a plurality of computers on the blockchain system to implement the action to contain the malware based on the smart contract; and in response to the consensus being reached by the plurality of computers, transmitting, from the blockchain system to the network resource, directions to implement the action on the network resource as specified by the smart contract.
10. The computer program product of claim 9 , wherein the method further comprises: generating a network resource graph of the network resource, wherein the network resource graph depicts connections between nodes that depict network devices in the network resource; generating a dominant nodes graph of dominant nodes in the network resource graph, wherein the dominant nodes graph depicts a dominance frontier of the nodes in the network resource graph; applying a set of network resource policies on the dominant nodes graph in order to determine a set of paths from a source node to a destination node; applying a dominance frontier analysis from the source node to calculate sets of nodes in the network resource that can be affected by the malware; utilizing a machine learning system to determine potential malware spread pathways of the malware through resources depicted by the sets of nodes in the network resource that can be affected by the malware; determining a particular set of nodes, from the sets of nodes in the network resource that can be affected by the malware, that represent resources that need to be shut down based on a cost based model using a K-min cut algorithm; and shutting down the resources that are represented by the determined particular set of nodes.
11. The computer program product of claim 9 , wherein the network resource is a datacenter.
A system and method for managing network resources, particularly in datacenter environments, addresses the challenge of efficiently allocating and optimizing resource utilization. The invention involves a computer program product that monitors and controls network resources, such as datacenters, to improve performance and reduce operational costs. The system includes a monitoring module that tracks resource usage, including computational, storage, and network bandwidth metrics, to identify inefficiencies or bottlenecks. An optimization module then analyzes this data to generate recommendations or automatically adjust resource allocation, ensuring optimal performance and cost-effectiveness. The system may also incorporate predictive analytics to anticipate future resource demands and proactively adjust configurations. By focusing on datacenters, the invention enhances scalability, reliability, and energy efficiency, addressing common issues in large-scale computing environments. The solution is designed to integrate with existing infrastructure, providing seamless management without requiring significant hardware upgrades. This approach helps organizations maximize their datacenter investments while maintaining high availability and performance standards.
12. The computer program product of claim 9 , wherein the network resource is a component of a datacenter, and wherein the network resource is from the group consisting of a computer, a network port, a switch and a virtual machine used by the datacenter.
13. The computer program product of claim 9 , wherein the plurality of computers are used by a plurality of users that direct the blockchain system to establish the smart contract, wherein the network resource is a datacenter, and wherein the plurality of users comprise a client user of the datacenter, a cloud administrator of a cloud upon which the datacenter resides, a compliance officer for the datacenter, and a security manager for the datacenter.
14. The computer program product of claim 9 , wherein the smart contract codifies policies and requirements for network resource availability, network resource shutdown, and network resource incidence responses for the network resource.
15. The computer program product of claim 9 , wherein ledgers on the blockchain system keep track of malware propagation information about the malware, probabilistic indicators of compromise about the malware, and identification information about the malware.
16. The computer program product of claim 9 , wherein the computer environment is a cloud environment.
17. The computer program product of claim 9 , wherein the program instructions are provided as a service in a cloud environment.
18. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable non-transitory storage mediums, and program instructions stored on at least one of the one or more computer readable non-transitory storage mediums for execution by at least one of the one or more processors via at least one of the one or more computer readable memories, the stored program instructions executed to perform a method comprising: establishing a smart contract on a blockchain system for a network resource in a computer environment for an action to be performed on the network resource if a malware is detected in the network resource, wherein the computer environment is a network system that is a separate system from the blockchain system; in response to the malware being detected in the network resource, determining whether a consensus is reached by a plurality of computers on the blockchain system to implement the action to contain the malware based on the smart contract; and in response to the consensus being reached by the plurality of computers, transmitting, to the network resource, directions to implement the action on the network resource as specified by the smart contract.
19. The computer system of claim 18 , wherein the method further comprises: generating a network resource graph of the network resource, wherein the network resource graph depicts connections between nodes that depict network devices in the network resource; generating a dominant nodes graph of dominant nodes in the network resource graph, wherein the dominant nodes graph depicts a dominance frontier of the nodes in the network resource graph; applying a set of network resource policies on the dominant nodes graph in order to determine a set of paths from a source node to a destination node; applying a dominance frontier analysis from the source node to calculate sets of nodes in the network resource that can be affected by the malware; utilizing a machine learning system to determine potential malware spread pathways of the malware through resources depicted by the sets of nodes in the network resource that can be affected by the malware; determining a particular set of nodes, from the sets of nodes in the network resource that can be affected by the malware, that represent resources that need to be shut down based on a cost based model using a K-min cut algorithm; and shutting down the resources that are represented by the determined particular set of nodes.
20. The computer system of claim 18 , wherein ledgers on the blockchain system keep track of malware propagation information about the malware, probabilistic indicators of compromise about the malware, and identification information about the malware.
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April 13, 2021
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